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Open AccessArticle

Reinforcement Learning Based Query Routing Approach for P2P Systems

by Fawaz Alanazi and Taoufik Yeferny *,†
College of Science, Northern Border University, Arar 73222, Saudi Arabia
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Future Internet 2019, 11(12), 253; https://doi.org/10.3390/fi11120253
Received: 19 November 2019 / Revised: 4 December 2019 / Accepted: 5 December 2019 / Published: 9 December 2019
Peer-to-peer (P2P) systems have offered users an efficient way to share various resources and access diverse services over the Internet. In unstructured P2P systems, resource storage and indexation are fully distributed among participating peers. Therefore, locating peers sharing pertinent resources for a specific user query is a challenging issue. In fact, effective query routing requires smart decisions to select a certain number of peers with respect to their relevance for the query instead of choosing them at random. In this respect, we introduce here a new query-oriented approach, called the reinforcement learning-based query routing approach (RLQR). The main goal of RLQR is to reach high retrieval effectiveness as well as a lower search cost by reducing the number of exchanged messages and contacted peers. To achieve this, the RLQR relies on information gathered from previously sent queries to identify relevant peers for forthcoming queries. Indeed, we formulate the query routing issue as the reinforcement learning problem and introduce a fully distributed approach for addressing it. In addition, RLQR addresses the well-known cold-start issue during the training stage, which allows it to improve its retrieval effectiveness and search cost continuously, and, therefore, goes quickly through the cold-start phase. Performed simulations demonstrate that RLQR outperforms pioneering query routing approaches in terms of retrieval effectiveness and communications cost. View Full-Text
Keywords: query routing; P2P; reinforcement learning; cold-start query routing; P2P; reinforcement learning; cold-start
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Alanazi, F.; Yeferny, T. Reinforcement Learning Based Query Routing Approach for P2P Systems. Future Internet 2019, 11, 253.

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